TBIRD technology could help image black holes’ photon rings
The Lincoln Laboratory-developed laser communications payload operates at the data rates required to image these never-before-seen thin halos of light.
Can robots learn from machine dreams?
MIT CSAIL researchers used AI-generated images to train a robot dog in parkour, without real-world data. Their LucidSim system demonstrates generative AI's potential for creating robotics training data.
J-PAL North America announces new evaluation incubator collaborators from state and local governments
Selected LEVER collaborators will work with the organization to develop an evaluation of their respective programs that alleviate poverty.
3 Questions: Inverting the problem of design
MIT and IBM researchers are creating linkage mechanisms to innovate human-AI kinematic engineering.
A causal theory for studying the cause-and-effect relationships of genes
By sidestepping the need for costly interventions, a new method could potentially reveal gene regulatory programs, paving the way for targeted treatments.
A new approach to modeling complex biological systems
MIT engineers’ new model could help researchers glean insights from genomic data and other huge datasets.
Empowering systemic racism research at MIT and beyond
Researchers in the MIT Initiative on Combatting Systemic Racism are building an open data repository to advance research on racial inequity in domains like policing, housing, and health care.
A faster, better way to train general-purpose robots
Inspired by large language models, researchers develop a training technique that pools diverse data to teach robots new skills.
Making it easier to verify an AI model’s responses
By allowing users to clearly see data referenced by a large language model, this tool speeds manual validation to help users spot AI errors.
Study finds mercury pollution from human activities is declining
Models show that an unexpected reduction in human-driven emissions led to a 10 percent decline in atmospheric mercury concentrations.
Modeling relationships to solve complex problems efficiently
Associate Professor Julian Shun develops high-performance algorithms and frameworks for large-scale graph processing.
How AI is improving simulations with smarter sampling techniques
MIT CSAIL researchers created an AI-powered method for low-discrepancy sampling, which uniformly distributes data points to boost simulation accuracy.
New security protocol shields data from attackers during cloud-based computation
The technique leverages quantum properties of light to guarantee security while preserving the accuracy of a deep-learning model.
Research quantifying “nociception” could help improve management of surgical pain
New statistical models based on physiological data from more than 100 surgeries provide objective, accurate measures of the body’s subconscious perception of pain.
Study: AI could lead to inconsistent outcomes in home surveillance
Researchers find large language models make inconsistent decisions about whether to call the police when analyzing surveillance videos.